<h1 id="texas-congressional-districts">2020 Texas Congressional
Districts</h1>
<h2 id="redistricting-requirements">Redistricting requirements</h2>
<p>In Texas, districts must meet US constitutional requirements, but
there are <a
href="https://redistricting.capitol.texas.gov/reqs#congress-section">no
state-specific statutes</a>.</p>
<h3 id="algorithmic-constraints">Algorithmic Constraints</h3>
<p>We enforce a maximum population deviation of 0.5%.</p>
<h2 id="data-sources">Data Sources</h2>
<p>Data for Texas comes from the ALARM Project’s <a
href="https://alarm-redist.github.io/posts/2021-08-10-census-2020/">2020
Redistricting Data Files</a>.</p>
<h2 id="pre-processing-notes">Pre-processing Notes</h2>
<p>We estimate CVAP populations with the <a
href="https://github.com/christopherkenny/cvap"><code>cvap</code></a> R
package. We also pre-process the map to split it into clusters for
simulation, which has a slight effect on the types of district plans
that will be sampled.</p>
<h2 id="simulation-notes">Simulation Notes</h2>
<p>We sample 50,000 districting plans for Texas across two independent
runs of the SMC algorithm, then filter down to 5,000 total plans. Due to
the size and complexity of Texas, we split the simulations into multiple
steps.</p>
<h3 id="clustering-procedure">1. Clustering procedure</h3>
<p>First, we run simulations in three major metropolitan areas: Greater
Houston, a combination of Greater San Antonio and Austin, and
Dallas-Fort Worth. We use collections of counties that define the
Metropolitan Statistical Areas. The counties in each cluster are those
in each Census MSA:</p>
<ul>
<li><p>Houston–The Woodlands–Sugar Land: Austin, Brazoria, Chambers,
Fort Bend, Galveston, Harris, Liberty, Montgomery, Waller.</p></li>
<li><p>Austin–Round Rock-Georgetown: Bastrop, Caldwell, Hays, Travis,
Williamson.</p></li>
<li><p>San Antonio–New Braunfels: Atascosa, Bandera, Bexar, Comal,
Guadalupe, Kendall, Medina, Wilson.</p></li>
<li><p>Dallas–Fort Worth–Arlington: Collin, Dallas, Denton, Ellis, Hunt,
Kaufman, Rockwall, Johnson, Parker, Tarrant, Wise.</p></li>
</ul>
<p>These simulations run the SMC algorithm within each cluster with a
0.25% population tolerance. Because each cluster will have leftover
population, we apply an additional constraint that incentivizes leaving
any unassigned areas on the edge of these clusters to avoid
discontiguities.</p>
<p>In each cluster, we apply hinge Gibbs constraints of strength 3 to
encourage the formation of Hispanic CVAP opportunity districts. In
Houston, we also apply a hinge Gibbs constraint of strength 3 to
encourage the formation of Black CVAP opportunity districts. These
districts nudge the formation of opportunity districts are above 35%,
and penalize districts with minority populations above 70%.</p>
<h3 id="combination-procedure">2. Combination procedure</h3>
<p>Then, these partial map simulations are combined to run statewide
simulations. We again apply Gibbs hing constraints to encourage the
formation of minority opportunity districts.</p>
<h2 id="contents">Contents</h2>
<ul>
<li><code>TX_cd_2020_stats.csv</code> contains summary statistics on the
sampled redistricting plans</li>
<li><code>TX_cd_2020_plans.rds</code> is a compressed
<code>redist_plans</code> object, which contains the matrix of
precinct/block assignments and may be used for further analysis.</li>
<li><code>TX_cd_2020_map.rds</code> is a compressed
<code>redist_map</code> object, which contains the precinct/block
shapefile and demographic data.</li>
</ul>
<p>Both the <code>redist_plans</code> and <code>redist_map</code> object
are intended to be used with the <a
href="https://alarm-redist.github.io/redist/">redist package</a>.</p>
<h3 id="codebook-for-summary-statistics">Codebook for summary
statistics</h3>
<ul>
<li><code>draw</code>: unique identifier for each sample. Non-numeric
draw names are real-world plans, e.g., <code>cd_2010</code> for an
enacted 2010 plan.</li>
<li><code>district</code>: a district identifier. District numbers
roughly match those in the enacted plan, but the correspondence is not
perfect.</li>
<li><code>chain</code>: a number identifying the run of the
redistricting algorithm used to produce this draw. Used for diagnostic
purposes.</li>
<li><code>pop_overlap</code>: a number indicating the fraction of people
in this plan who reside in the same-numbered district in the enacted
plan.</li>
<li><code>total_pop</code>: the total population of each district.</li>
<li><code>total_vap</code>: the total voting-aged population of each
district.</li>
<li><code>pop_*</code>, <code>vap_*</code>: total (voting-aged)
population within racial and ethnic groups for each district. Variable
codes documented <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>plan_dev</code>: the maximum population deviation among
districts in the plan. Computed as
<code>max(abs(distr_pop - target_pop)/target_pop)</code>.</li>
<li><code>comp_edge</code>: compactness, as measured by the fraction of
internal edges kept. Higher values indicate more compactness.</li>
<li><code>comp_polsby</code>: compactness, as measured by the
Polsby-Popper score. Higher values indicate more compactness.</li>
<li><code>county_splits</code>: the number of counties which belong to
more than one district.</li>
<li><code>muni_splits</code>: the number of Census Designated Places
which belong to more than one district.</li>
<li><code>*_##_dem_*</code>, <code>*_##_rep_*</code>: vote counts for
statewide Democratic and Republican candidates in a certain election.
More information <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>adv_##</code>, <code>arv_##</code>: average vote counts for
statewide Democratic and Republican candidates in a certain year. More
information <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>ndv</code>, <code>nrv</code>: averages of the
<code>adv_##</code> and <code>arv_##</code> variables across all
available elections.</li>
<li><code>ndshare</code>: normal Democratic share, computed as
<code>ndv / (ndv + nrv)</code></li>
<li><code>e_dvs</code>: average Democratic vote share, computed as the
average of the Democratic vote share when first scored under each
statewide election.</li>
<li><code>pr_dem</code>: probability seat is represented by a Democrat;
calculated as the fraction of statewide elections under which the
district had a majority Democratic share.</li>
<li><code>e_dem</code>: expected number of Democratic seats for the
plan; equivalent to summing the <code>pr_dem</code> values across
districts</li>
<li><code>pbias</code>: partisan bias at 50% vote share, averaged across
all available elections. Positive values indicate Republican bias.</li>
<li><code>egap</code>: the efficiency gap, averaged across all available
elections. Positive values indicate Republican bias.</li>
</ul>
